# deepseek生成的代码，暂不使用
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# 加载教师和学生模型
teacher = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-llm-7b-base")
student = AutoModelForCausalLM.from_pretrained("deepseek-coder-1.3b-instruct")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-llm-7b-base")

# 蒸馏参数
T = 3.0  # 温度
alpha = 0.7  # 软目标权重

# 蒸馏循环
for batch in dataloader:
    inputs = tokenizer(batch["text"], return_tensors="pt", padding=True)

    # 教师预测（禁用梯度）
    with torch.no_grad():
        teacher_logits = teacher(**inputs).logits

    # 学生预测
    student_logits = student(**inputs).logits

    # 计算软目标损失
    soft_target = torch.nn.functional.kl_div(
        input=torch.log_softmax(student_logits / T, dim=-1),
        target=torch.softmax(teacher_logits / T, dim=-1),
        reduction="batchmean"
    ) * (T ** 2)

    # 计算硬目标损失（若有标签）
    labels = inputs["input_ids"]  # 自回归语言建模
    hard_target = torch.nn.functional.cross_entropy(
        student_logits.view(-1, student_logits.size(-1)),
        labels.view(-1)
    )

    # 总损失
    loss = alpha * soft_target + (1 - alpha) * hard_target

    # 反向传播
    loss.backward()
    optimizer.step()